A Deep Active Contour Model for Delineating Glacier Calving Fronts

• Published in: IEEE Transactions on Geoscience and Remote Sensing
• Main Authors: Heidler, Konrad, Mou, Lichao, Loebel, Erik, Scheinert, Mirko, Lefèvre, Sébastien, Zhu, Xiao Xiang
• Other Authors: Technische Universität Munchen - Technical University Munich - Université Technique de Munich (TUM), Technische Universität Dresden = Dresden University of Technology (TU Dresden), Observation de l’environnement par imagerie complexe (OBELIX), SIGNAL, IMAGE ET LANGAGE (IRISA-D6), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2023
• Subjects: Active contours; edge detection; Greenland; glacier front; uncertainty; [SDE.IE]Environmental Sciences/Environmental Engineering; [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]; glacier
• Online Access: https://hal.science/hal-04430672; https://hal.science/hal-04430672/document; https://hal.science/hal-04430672/file/2307.03461.pdf; https://doi.org/10.1109/TGRS.2023.3296539

International audience This work has been accepted by IEEE TGRS for publication in a future issue. Choosing how to encode a realworld problem as a machine learning task is an important design decision in machine learning. The task of glacier calving front modeling has often been approached as a semantic segmentation task. Recent studies have shown that combining segmentation with edge detection can improve the accuracy of calving front detectors. Building on this observation, we completely rephrase the task as a contour tracing problem and propose a model for explicit contour detection that does not incorporate any dense predictions as intermediate steps. The proposed approach, called "Charting Outlines by Recurrent Adaptation" (COBRA), combines Convolutional Neural Networks (CNNs) for feature extraction and active contour models for the delineation. By training and evaluating on several large-scale datasets of Greenland's outlet glaciers, we show that this approach indeed outperforms the aforementioned methods based on segmentation and edgedetection. Finally, we demonstrate that explicit contour detection has benefits over pixel-wise methods when quantifying the models' prediction uncertainties. The project page containing the code and animated model predictions can be found at https://khdlr.github.io/COBRA/.